Using data digits to identify fabricated data

In this article we discuss four cases of research misconduct reported by the Office of Research Integrity (ORI) at the U.S. Public Health Service (PHS) in the Federal Register, focusing on how ORI uses digits in data other than the leading digit as evidence that data is fabricated or falsified. In the next article of this series, we examine more closely the probative value of using significant digits in ORI's analysis     

Meta data

Meta data provides an architecture or framework describing the user's data within a data environment. This architecture should provide a precise, coherent and logical structure that “paints a picture” of the data. It also should describe how the data internal to the data environment is interfaced to the external world. Satisfying the different needs of users and implementers, obtaining all the desirable qualities of meta data, while interfacing with various data systems is certainly a Herculean task for meta data. Basically, there seem to be three approaches to managing meta data: meta data bridges; meta data repositories; and intelligent software agents. One meta data repository trying to catch on is Microsoft's Repository (MDC-OIM). It uses the Open Information Model (OIM) as its base. The Meta Data Coalition (MDC) and the Object Management Group (OMG) have refined this OIM model. Both groups have also proposed it as the common standard for meta data exchange. The goal of the MDC-OIM repository is to allow creation, reuse and exchange of meta data across multiple hardware and software platforms. This includes the meta data integration of all tools, applications and databases for each individual platform     

基于数据驱动的稀疏虚假数据注入攻击

提出了一种基于数据驱动的稀疏虚假数据注入攻击策略。攻击策略分为3个阶段:第一阶段,基于稀疏优化技术对窃听的数据进行预处理以剔除异常值;第二阶段,基于平行因子分解算法推断不完整的系统信息矩阵;第三阶段,根据推断的系统矩阵,使用凸优化的方法求解稀疏攻击向量。仿真实验结果表明,当存在异常值时,传统的攻击策略无法成功实施,而所提攻击策略仍能成功地实施稀疏虚假数据注入攻击。     

大数据技术在电网数据上的应用

为了有效预测电厂机组出力情况,不仅要采用先进的预测方法,还要对机组出力数据进行合理的预处理.提出了电厂机组出力数据分析的一整套流程,包括数据集成、异常值预处理、数据可视化和电厂机组出力预测.以某省2018年机组出力数据为研究对象,选取一个燃煤电厂为例.经过数据集成、数据预处理和可视化展示,并用长短期记忆(LSTM)神经网络对电厂出力进行预测,LSTM预测得到的平均绝对百分比误差(MAPE)为10.90％,预测结果优于误差反向传播(BP)神经网络,BP神经网络预测得到的MAPE为11.61％.说明经过预处理的机组出力数据再用LSTM模型预测能达到良好的预测准确度.     

基于数据挖掘算法数据模型实现大数据分析的方法研究

针对大型数据库中的海量数据筛选困难的问题,设计了基于数据挖掘算法模型分析大数据的方法。通过K-means聚类算法,使得用户从不同的样本数据中,根据大数据样本的某些属性或某类特征,在输出的数据中,再采用BP神经网络模型对获取的聚类数据进一步训练、计算,这种方法能够映射、处理不同数据之间聚类种类大数据之间的复杂非线性关系,提高数据处理精度和用户挖掘大数据的能力。试验结果表明,设计的方法能够使用户更加便捷地处理大数据,提高了大数据应用的效率。     

多通道高速数据采集故障录波装置

给出了一种多通道高速数据采集和故障录波器装置的设计方法。同时介绍了相关的PC功能卡的特性技术指标和使用方法，包括高增益、高性能的多功能数据采集卡，PCL－818GH，16路光电隔离数字信号输入板PCL－782，双口CAN总线通讯卡PCL－841，文中还给出了关于信号输入调理板的设计方法，介绍了有关数据传输以及该数据采集和故障录波装置的主要特点。     

基于XML的电能质量标准数据共享模型及跨平台数据交换

IEEE提出的标准二进制电能质量数据格式(PQDIF)采用了通道定义和数据序列分离的数据结构,不仅降低了数据查询效率,而且读取、修改和结合应用程序实现复杂。在现有PQDIF数据模型的基础上,提出了一种用XML实现的电能质量数据模型:模型将测量类型、相位、序列值等以子对象方式嵌套于3种记录类型,通道定义和数据序列以索引相连,提高了数据查询效率;同时在XML模式定义中采用base64binary二进制储存PQ数据,有效地减少数据占用空间;最后,提出了基于模式转换的映射数据交换方法,任何2个XML文档解析出来的树结构元素都可以根据距离根节点的深度和数据字典进行语义自动匹配,精确度更高,处理速度也更快。基于以上数据交换模型和模式转换方法成功地实现了异构电能质量数据的交换,实践证明所提出的数据模型及实现方式具有良好的鲁棒性和可行性,能较好地实现电能质量数据的跨平台交换与共享。     

电力调度数据中心数据质量问题研究

结合调度中心数据特点和业务特点提出构建数据中心提高数据质量的一些控制方法,这些方法分别从电网数据的统计特性和业务特性(数据关联性特性)上对异常数据进行识别,并且基于这些方法给出了数据校验的模块设计,最后应用该数据校验模块到数据中心的系统架构中,提高了华东电力调度数据中心的数据质量.     

Storing magnetic data

Magnetic recording data storage innovation and product evolution is arguably higher than it has ever been. For example, the current record in high density recording is 23.8 Gbit/in², which was demonstrated by Seagate Technology. Data is stored by creating a pattern of magnetization in the media using a recording head. Basically, the head is a split ring-shaped core of easily magnetized material wrapped by a few turns of wire. When current flows in the wire, it induces a magnetic flux in the core and a field across the recording gap. Reversing the current's direction changes the direction of the magnetic field. Because the field lines spread out as they bridge the gap, they magnetize the media in a small zone near the gap. Since data is stored as binary digits, a pattern of current reversals can be coded to represent binary digits. For example, “1” might be represented by a magnetization reversal and “0” by the absence of a reversal. Data is read by sensing the fields that arise from the magnetization transition zones in the media. These fields are caused by a concentration of magnetic poles at the ends of each magnetized region. Since these fields point in the opposite direction to the media's magnetization, they are called “demagetizing fields” These fields extend beyond the media's surface. Thus, they can induce responses in a read head if it is brought close to the media     

Mobile data management

The management of data in the mobile computing environment offers new challenging problems. Existing software needs to be upgraded to accommodate this environment. To do so, the critical parameters need to be understood and defined. We have surveyed some problems and existing solutions     

Visualizing corporate data

Corporate databases have been recognized as strategic assets, and a successful corporation will make full use of its data resources to gain competitive advantage to better manage its business. Visualization is a key technology for extracting information from data, therefore, it is becoming increasingly important in our information rich society. It complements other analytic, model based approaches and exploits human pattern perception. Visualization can help users to navigate and explore the fast-growing number of data warehouses far more easily, and to rapidly discover the information hidden within volumes of data     

Peak load forecasting using multiple-year data with trend data processing techniques

This paper presents a regression-based daily peak load forecasting method using multiple-year data with trend cancellation and trend estimation techniques. Daily peak load heavily depends on daytime temperature and is influenced by the other weather factors such as humidity. Since the characteristic of the load is varying, peak loads just before a forecasting day are more significant for the forecasting. The regression model can represent relationships between these weather factors and peak loads. However, the forecasting model is sometimes not adequate for precise load forecasting. The regression model is well matched with the late data, but the model causes large forecasting errors in transitional seasons because of seasonal change of load characteristics. In order to forecast precisely through a year, a method of using seasonal or whole year data from past years is proposed. In this paper, two kinds of trend data processing techniques are described. The first is trend cancellation. The second is trend estimation. The trend cancellation technique removes annual load growth by means of division or subtraction processes with morning load on the forecasting day. The trend estimation technique estimates the trend between the forecasting year's load and the past year's load by using the variable transformation techniques. The performance of both techniques, verified with simulations on actual load data, is also described. © 1998 Scripta Technica, Electr Eng Jpn, 124(1): 7–16, 1998     

Spatial-temporal traffic data analysis based on global data management using MAS

The spatial-temporal traffic data analysis based on global data management is a newly developed and crucial approach to help traffic managers having the global view of urban traffic status in the level of road network, which is very clearly useful in traffic control and route guidance. The multiagent systems are used in traffic data management with full consideration of the characteristics of traffic data and the cooperation and workflow among them. In software implementation of data management, the agent-based common object request broker architecture is adopted taking the distributed urban traffic data in the large area under network environments into account. Based on the global traffic data, the approach of visualized spatial-temporal analysis is then induced. The similarity of traffic data is analyzed first for each link and its profile is achieved to undertake the primary processing of urban traffic data. Furthermore, analysis results are shown on the basis of the geographic information systems for transportation. The two types of visualization, pseudocolor and contour maps, are adopted in the demonstration to display the traffic status graphically and its changing frames. Among the applications in some big cities in China, the case of urban traffic analysis for Beijing is studied to demonstrate the implementation of the approach.     

地区电力调度数据仓库建模与数据处理

分析电力调度中心各业务系统中的数据,将系统数据分为设备台帐、周期采样和事件动作3种类型,后2种数据中大部分为事实数据,其数据量占数据仓库的90%以上。提出其共享维度建立数据模型的建模思路,数据仓库的数据来源是任何与需要分析主题有关的地区电力调度数据,在数据抽取、转换、清洗、装载(ETL)的过程进行数据过滤、重新选择粒度、统一编码等清洗与转换后存入数据仓库中,形成按照主题定义的数据子集或数据子立方体结构。数据仓库维度建模有第三范式(3NF)和星型2种模式,3NF模型适用于地调数据仓库中心建模;星型模型适用于数据集市中建模,利于信息检索。论述了数据仓库维度模型中的基本表——事实表和维度表,并对地区电力调度中心数据仓库的测点、日期与时间维度表进行了设计,说明了ETL数据仓库中的相关数据是操作环境对应数据,首先将逻辑数据模型转换为物理数据模型后,进行扩展性标识语言(XML)描述实现异构系统的数据共享。提出要考虑故障发生与天气、温度、湿度等气象条件和负荷变化之间的联系,给出了通过人工神经网络等特定数据挖掘方法的基本流程。     

Grid-free interactive and automated data processing for MR chemical shift imaging data

Purpose  

Today’s available chemical shift imaging (CSI) analysis tools are based on Fourier transform of the entire data set prior to interactive display. This strategy is associated with limitations particularly when arbitrary voxel positions within a 3D spatial volume are needed by the user. In this work, we propose and demonstrate a processing-resource-efficient alternative strategy for both interactive and automated CSI data processing up to three spatial dimensions.     

基于多数据源校验的电网EMS数据质量在线监控方法及其实现

为了解决智能电网EMS系统因通信通道或设备误发、丢失以及延迟发送数据等原因而导致的数据通信故障,提出了一种基于多数据源校验的电网EMS数据质量在线监控方法。设计了一体化信息平台,采用通信监听技术,对来自不同数据源的同一个量测值进行比对,发现可能存在的数据质量故障,并对故障点进行诊断。借助一体化信息平台分布式架构特点,避免了传统集中式数据比对导致的效率低的问题。实际部署应用表明,基于分布式架构的多数据源比对平台,为电网EMS系统数据质量控制与快速故障诊断提供了一种自动校验的技术手段和方法,也为生产调度人员提供了一款功能强大的平台化分析诊断工具。     

基于预测模型的发电厂异常数据辨识方法

发电统计数据是我国实施电力监管的重要依据,其中的发电厂用电率作为反映电厂生产效能、论证节能降耗情况的关键指标,验证其上报数据的真实性和准确性十分重要。为此,提出一种基于预测模型的发电厂异常数据辨识方法。该方法首先利用Adaboost改进非线性有源自回归模型(NARX)神经网络构建发电厂用电率预测模型,通过不断引入上报值以动态的方式对当前时刻发电厂用电率进行预测。当发电厂用电率时间序列出现突变时,残差时间序列会出现明显的增大或减小,进而利用孤立森林算法得到各残差向量组的异常分值从而辨识出异常点。最后,利用该方法对注入了虚假数据的实际发电数据进行辨识,验证了所提方法的有效性。     

基于区块链与数据湖的电力数据存储与共享方法

针对电力物联网边设备之间、云主站平台营配调各系统之间的数据存储和共享的需求,提出了一种基于区块链与数据湖的电力数据存储与共享方法。首先,设计边缘层分布式电力数据存储架构,通过环签名和CryptoNote协议对边设备存储节点间的数据交互进行加密,实现双方身份认证,并基于区块链智能合约实现电力数据安全存储系统中节点间的数据共享。然后,基于数据湖与智能合约构建营配调平台间的数据共享和访问控制模型,其中在区块链中存储数据的哈希值,并在可信执行环境下将加密后的原始数据存储在数据湖中,以实现数据跨平台跨域安全共享和访问。最后,搭建实验平台对所提方法进行论证,结果表明,所提方法的最高存储延迟时间不高于25ms、并且吞吐量和安全性也较高。     

基于部分数据块复用的SoC测试数据压缩方法

提出了一种基于部分数据块复用及统计的测试数据压缩方法。以测试向量为单位,首先寻找出一个最优参考数据块,再利用数据块的相关性及部分数据块可复用性,并按编码规则对其进行编码压缩。它的解压结构只由一个有限状态机(FSM)控制,其结构简单、硬件开销较小。在基准电路上进行的实验表明,本文提出的方案能够有效的压缩测试数据,较同类编码方法有更高的压缩效率。